mlllogis: Log-logistic distribution maximum likelihood estimation

In univariateML: Maximum Likelihood Estimation for Univariate Densities

Description

The maximum likelihood estimate of shape and rate are calculated by transforming the data back to the logistic model and applying mllogis.

Usage

mlllogis(x, na.rm = FALSE, ...)

Arguments

x	a (non-empty) numeric vector of data values.
na.rm	logical. Should missing values be removed?
...	passed to mllogis.

Details

For the density function of the log-logistic distribution see Loglogistic

Value

mlllogis returns an object of class univariateML. This is a named numeric vector with maximum likelihood estimates for shape and rate and the following attributes:

model	The name of the model.
density	The density associated with the estimates.
logLik	The loglikelihood at the maximum.
support	The support of the density.
n	The number of observations.
call	The call as captured my match.call

References

Kleiber, C. and Kotz, S. (2003), Statistical Size Distributions in Economics and Actuarial Sciences, Wiley.

Klugman, S. A., Panjer, H. H. and Willmot, G. E. (2012), Loss Models, From Data to Decisions, Fourth Edition, Wiley.

Dutang, C., Goulet, V., & Pigeon, M. (2008). actuar: An R package for actuarial science. Journal of Statistical Software, 25(7), 1-37.

See Also

Loglogistic for the log-logistic density.

Examples

mllnorm(precip)

Example output

Maximum likelihood estimates for the Lognormal model 
meanlog    sdlog  
 3.4424   0.5247  

univariateML documentation built on Jan. 25, 2022, 5:09 p.m.